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Using text mining to analyze reflective essays from Japanese medical students after rural community placement

机译:在农村社区安置后,使用文本挖掘分析日本医学生的反思论文

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Following community clinical placements, medical students use reflective writing to discover the story of their journey to becoming medical professionals. However, because of assessor bias analyzing these writings qualitatively to generalize learner experiences?may be problematic. This study uses a process-oriented text mining approach to better understand meanings of learner experiences by connecting key concepts in extended student reflective essays. Text mining quantitative analysis is used on self-evaluative essays (n?=?47, unique word count range 43–575) by fifth-year students at a regional quota-system university in Japan that specializes in training general practitioners for underserved communities. First, six highly-occurring key words were identified: patient, systemic treatment, locale, hospital, care, and training. Then, standardized keyword frequency analysis robust to overall essay length and keyword volume used individual keywords as “nodes” to calculate per-keyword values for each essay. Finally, Principle Components Analysis and regression were used to analyze key word relationships. Component loadings were strongest for the keyword area, indicating most shared variance. Multiply regressing three of the remaining keywords hospital, systemic treatment, and training yielded R2?=?0.45, considered high for this exploratory study. In contrast, direct patient experience for students was difficult to generalize. Impressions of the practicing area environment were strongest in students, and these impressions were influenced by hospital workplace, treatment provision, and training. Text mining can extract information from larger samples of student essays in an efficient and objective manner, as well as identify patterns between learning situations to create models of the learning experience. Possible implications for community-based clinical learning may be greater understanding of student experiences for on-site precepts benefitting their roles as mentors.
机译:在社区临床展示之后,医学生使用反思写作来发现他们成为医疗专业人士的旅程的故事。但是,由于评估员偏见分析这些着作,定性地概括了学习者体验?可能是有问题的。本研究采用了一种以过程为导向的文本挖掘方法来更好地了解学习者体验的含义,通过在扩展学生反思论文中连接密钥概念。文本挖掘定量分析用于自我评价论文(N?=?47,独特的单词数范围43-575),在日本区域配额系统的第五年度学生专门培养欠缺社区的全科医生。首先,确定了六种高度发生的关键词:患者,全身治疗,地区,医院,护理和培训。然后,标准化的关键字频率分析强大到整体论文长度和关键字卷使用单个关键字作为“节点”来计算每个文章的每个关键字值。最后,使用原理分析分析和回归来分析关键词关系。组件加载对于关键字区域最强,表示大多数共享方差。乘以剩余关键词,全身治疗和训练的三次回归3次r2?=?0.45,认为这项探索性研究很高。相比之下,对学生的直接患者体验难以概括。练习区域环境的印象在学生中最强,这些印象受到医院工作场所,治疗条款和培训的影响。文本挖掘可以以有效和客观的方式从学生散文的较大样本中提取信息,以及识别学习情况之间的模式,以创造学习体验的模型。对基于社区的临床学习可能的影响可能会更加了解学生的现场急救的经验,这些初步的旨在使其成为导师的角色。

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